“ Information ” is not only about the world; it is in the world, and so once Google ’ s platform archive has reached a threshold equilibrium, the company ’ s mission statement suggests that it should retool the world to make it generate more information, so that the world itself can be better organized. In some cases, making information physical is indistinguishable from making physical machines more computational; the impetus for hybridization can begin from either side. In 2013 – 2014, Google went on an extraordinary buying spree, acquiring many of the most innovative robotics companies, many working in areas not directly related to the company ’ s existing services and offerings. Google ’ s new zoo of machines represents a diverse range of physical activities that could be programmed and monitored by its platform; together these companies make robots that can run, jump, carry heavy things, fly, make things, take things apart, and manage the temperature of your house based on your daily routines. It is reported that Google ’ s robotics ventures will be one of the occupants of the Hangar One megastructure at Moffett Field, leased from NASA ’ s Ames Research Center in Mountain View, CA, but with the chief architect of the Android mobile operating system, Andy Rubin, initially in charge of the project, we surmise that its mission is perhaps more to link robots together on a common operating system than to engineer their bodies. Rubin has since left Google, replaced by James Kuffner, but the longer term strategy may be in place: organize and make accessible the robotic phylum. Our anthropocentric predispositions often lead us think of a robot in the singular, as a synthetic person that contains its program just like a human thinks that it contains a will and a personality. Platform-based robotics, however, suggest other avenues of evolution. Today, ROS (robot operating system) is one existing platform of tools, libraries, and frameworks for adding software-based functionality to robots in ways similar to how one would program a software-only application; however; it cannot match what something like a robust “ Android for robots ” could do as part of the Google Cloud Polis model. Robotic mechanisms at varying scales would not only execute programs, but could sense, correlate, and report data on their interactions with the world, feeding the platform ’ s appetite for information to search, organize, and optimize. Each mechanism would also be in essence another User of the Google platform, and like all other Users, it would search and be searched, make use of the information that is there, and generate more information for other Users. Furthermore, as a model User, it is plural; its own intelligence is networked with the platform and with the wider collective of robotic and human Users with whom it collaborates. Should any one mechanism encounter something unknown, it could access a live API (application programming interface) or another more efficient technique and register the intelligence necessary to make decisions and actions. Over time, as each encounters different things and modifies its programming to accommodate them, two robot mechanisms born of the same model begin to diverge and even speciate. (More on this in the concluding chapter.) In the meantime, instead of new forms and behaviors, as platform information becomes more physical, its initial accomplishments may to “ roboticize ” machines, processes, techniques, behaviors, and systems as we already find them. The results are not unlike skeuomorphic interface designs where digital icons are made to resemble everyday objects and so allegorize how human Users (and designers) understand their machine ’ s functions to work. It will take some time for platform robotics to invent new infrastructural systems that are unique to how its capacities can be designed instead of merely automating what already exists.
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